Fractional CTO for AI Startup — ship AI products, not demos
Every startup is an “AI startup” now — which means the winners are decided by engineering, not pitch decks. The gap between a compelling demo and a production AI product is enormous: evaluation, cost control, latency, hallucinations, data pipelines. A fractional CTO for your AI startup closes that gap with leadership that understands both the models and the business.
I'm Omer Muneer Qazi, a Fractional CTO & Solutions Architect with 15+ years of engineering leadership and 100+ projects across 6 countries, including real-time voice intelligence, LLM integrations, and custom ML pipelines. I help AI founders turn model capabilities into reliable products customers pay for. Explore my AI work and engagement models.
AI product leadership, end to end
LLM integration strategy
Model selection (frontier vs. open-source vs. fine-tuned), provider strategy, and fallback planning — so you're not hostage to one API's pricing change or deprecation.
RAG & data pipeline architecture
Retrieval-augmented generation done properly: chunking strategy, embedding choices, vector database selection, and the evaluation loops that keep answer quality from silently degrading.
Evaluation & safety frameworks
Benchmarks, red-teaming basics, and guardrails proportionate to your risk — because “the model usually gets it right” is not a production strategy.
Inference cost control
Token economics, caching, model routing (small models for easy queries), and usage monitoring. AI margins die at scale without deliberate cost architecture.
AI product roadmap
Sequencing what the models can actually do reliably today versus what's a research project — so your roadmap promises what engineering can deliver.
Build-vs-buy AI decisions
When to use an API, when to fine-tune, when to build custom: I evaluate the trade-offs against your data moat, latency needs, and unit economics.
From demo to durable product
A structured engagement with no surprises — you’ll always know what’s happening and what’s next.
Discovery call
Your AI use case, your current approach, and the gap between demo and production. I'll give you an honest read on what's hard versus what's hype.
AI architecture review
I audit your pipeline: prompts, retrieval, evaluation, costs, latency. You get a written report on what's production-ready and what isn't.
Production roadmap
A sequenced plan to harden the product: evals first, then cost optimization, then scale — tied to the milestones your business needs.
Ongoing AI leadership
Part-time CTO presence as you ship: model upgrade decisions, incident response for AI failures, and keeping the roadmap honest as capabilities evolve.
Why AI founders hire Omer
AI products fail in production for unglamorous reasons: no evals, runaway costs, latency nobody measured. With 15+ years, 100+ delivered projects, and $25M+ in enabled client revenue across 6 countries — including real-time voice AI and LLM-powered products — I bring the engineering discipline that turns AI demos into businesses. Previously with Phaedra Solutions and teams at Integriti, Napollo, Nabidios, Nello, and EverestX.
I stay current with what models can actually do, and skeptical about what vendors claim. See AI services, engagements, or talk through your AI roadmap.
Frequently asked questions
We have a working demo. How far are we from a real product?
Usually further than it feels. The honest answer depends on evals, cost per query, latency, and failure handling — I audit all four and give you a real gap analysis, not encouragement.
Should we fine-tune a model or use APIs?
APIs first, almost always — until your scale, latency, or data-privacy needs prove otherwise. Fine-tuning is expensive to do well and easy to do badly. I help you find the actual crossover point for your use case.
How do you control AI costs at scale?
Model routing, aggressive caching, prompt optimization, and usage monitoring with alerts. Most AI startups can cut inference costs 50–80% with architecture changes alone — no quality loss.
What about hallucinations and reliability?
Evals, guardrails, human-in-the-loop where stakes are high, and honest UX about uncertainty. Reliability is an engineering discipline: measure it, bound it, design around it.
Can you help us hire ML engineers?
Yes — and I'll often talk you out of it first. Most AI startups need strong software engineers with LLM experience, not research scientists. I define the right roles and vet candidates technically.
Turn your AI demo into a product
A free technical read on your AI pipeline: what's production-ready, what's fragile, and what it costs at scale.